24IS

Technical details

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library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

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kable(gpr) %>% scroll_box(width = "100%")

gdl_id	keep	crop_start	crop_end	thr_dur	extent_N	extent_W	extent_S	extent_E	map_scale	map_max_sample	map_margin	prob_map_s	prob_map_s_calib	prob_map_thr	shift_k	kernel_adjust	calib_lon	calib_lat	calib_1_start	calib_1_end	calib_2_start	calib_2_end	calib_2_lon	calib_2_lat	prob_light_w	thr_prob_percentile	thr_gs	thr_as	RingNo	scientific_name	common_name	mass	wing_span	Color	sta_id_winter
24IS	1	2019-08-01	2019-10-16 15:35:00	0	51	-18	5	16	4	300	30	1	1.3	0.9	0	1.4	8.692212	46.55493	2019-08-01	2019-09-16	NA	NA	NA	NA	0.09	0.95	120	100	N588304	Oenanthe oenanthe	Northern wheatear	NA	NA	#FECB52	NA

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.

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pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutlier | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  # geom_point(data = subset(pam$pressure, isoutlier), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Pressure calibration

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pressure_ts_bind <- do.call("rbind", shortest_path_timeserie) %>%
  filter(!is.na(sta_id))

pam$pressure %>%
  left_join(pressure_ts_bind %>% dplyr::select(c("date", "pressure0")), by = "date") %>%
  mutate(diff = ifelse(is.na(pressure0), 0, obs - pressure0)) %>%
  filter(sta_id > 0 & !isoutlier) %>%
  group_by(sta_id) %>%
  mutate(sta_id = paste0(sta_id, " (SD=", round(sd(diff), 2), " ; N=", n(), ")")) %>%
  ggplot(aes(x = diff)) +
  geom_histogram(aes(y = (..count..) / tapply(..count.., ..PANEL.., sum)[..PANEL..]), binwidth = .2) +
  facet_wrap(~sta_id) +
  scale_x_continuous(name = "Pressure Geolocator - best match ERA5 (hPa)") +
  scale_y_continuous(name = "Normalized histogram")

Light

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raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
  offset = 0, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

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hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

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 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

  par(mfrow = c(2, 3))
  for (i_s in long_id){
    twl_fl <- twl %>%
      filter(!deleted) %>%
      filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
    sun <-  solar(twl_fl$twilight)
    z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
    hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
    lines(fit_z, col = "red")
    xlab("Zenith angle")
  }

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

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  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
    if (nrow(twl_fl)>0){
    tsimageDeploymentLines(twl_fl$twilight,
                           lon = ts$lon[1], ts$lat[1],
                           offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
      
    }
  }

Stationay period information

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read_csv(paste0("../reports/figure_print/table_transition/transition_", params$gdl_id, ".csv")) %>% kable() %>% scroll_box(width = "100%")

…1	sta_id_s	sta_id_t	flight_duration	as_m	as_s	gs_m	gs_s	ws_m	ws_s	dist_m	dist_s	ws_m_support	ws_m_drift	start_flight	end_flight	sunrise	sunset	as_sp	gs_sp	ws_sp	dist_sp	ws_sp_support	ws_sp_drift	alt_min	alt_max	alt_mean	alt_med	alt_sumdabsdiff	alt_sumposdiff	gdl_id
1	1	2	0.5	26.97934	20.70084	25.28161	22.12765	3.872825	0.3869792	12.64081	11.06382	-1.4580895	3.587862	2019-08-25 02:30:00	2019-08-25 03:00:00	2019-08-25 04:03:47	2019-08-24 18:52:28	3.76797	0.00000	3.767970	0.00000	NA	NA	NA	NA	NA	NA	NA	NA	24IS
2	2	3	8.0	24.06175	10.90840	23.30085	11.40800	5.377384	1.7083335	186.40679	91.26399	-0.1528316	5.375212	2019-09-15 17:30:00	2019-09-16 01:30:00	2019-09-16 04:41:10	2019-09-15 18:08:26	35.55989	36.82306	3.370115	294.58450	1.395731	3.067509	1945.3098	4057.0632	2918.5213	2972.5831	6961.1029	3902.8739	24IS
3	3	4	17.0	60.88216	11.81063	64.78336	10.50064	13.299945	1.2433756	1101.31716	178.51095	5.1489680	12.262817	2019-09-16 18:00:00	2019-09-17 11:00:00	2019-09-17 04:42:57	2019-09-16 18:08:56	41.35821	47.76389	13.175809	811.98605	7.793431	10.623765	112.0134	3930.8401	2371.3943	2571.9045	18479.0584	7842.3539	24IS
4	4	5	4.0	38.64215	15.12835	35.97324	15.55860	8.824549	2.0736362	143.89294	62.23439	-1.6855461	8.662078	2019-09-17 18:00:00	2019-09-17 22:00:00	2019-09-18 04:47:14	2019-09-17 17:56:38	33.42300	26.74107	6.857087	106.96427	-6.637595	1.721039	-71.3019	1317.0302	753.5315	985.1288	2299.3030	1597.5093	24IS
5	5	6	11.5	36.26057	13.38826	61.85784	11.24755	28.571908	5.1941852	711.36521	129.34682	26.8997252	9.631134	2019-09-28 17:30:00	2019-09-29 05:00:00	2019-09-29 04:57:40	2019-09-28 17:42:44	35.21635	69.94260	34.925974	804.33989	34.825699	2.644687	360.5464	4474.7615	2898.8228	3167.9335	7725.5414	3591.4569	24IS
6	6	7	11.5	42.13209	16.37642	65.40593	20.22555	29.409624	6.5787717	752.16821	232.59382	25.7449787	14.216963	2019-09-29 18:00:00	2019-09-30 05:30:00	2019-09-30 05:16:09	2019-09-29 17:41:45	35.85819	69.83221	37.321774	803.07042	35.682977	10.938007	442.4284	3101.9670	1771.6879	1634.1970	6755.6640	3404.0059	24IS
7	7	8	9.5	46.13981	18.25180	47.00935	23.40254	23.580246	4.0147324	446.58881	222.32410	6.7755117	22.585846	2019-09-30 18:00:00	2019-10-01 03:30:00	2019-10-01 05:16:13	2019-09-30 17:58:41	34.68052	40.79118	23.483493	387.51620	12.412683	19.934888	460.3193	3901.7128	2278.8710	2233.9543	10032.7554	5074.1485	24IS
8	8	9	8.5	48.60386	16.35063	54.62970	16.98596	11.795870	4.0714654	464.35244	144.38066	6.9670057	9.518581	2019-10-02 18:30:00	2019-10-03 03:00:00	2019-10-03 05:16:14	2019-10-02 17:57:10	28.48829	35.80842	14.851577	304.37155	9.651771	11.287722	320.3994	3688.1971	2378.6247	3051.3260	8186.7076	3977.7571	24IS
9	9	10	1.5	33.40691	15.18007	34.86053	17.60808	9.408509	4.9004958	52.29079	26.41213	2.6929379	9.014884	2019-10-03 20:30:00	2019-10-03 22:00:00	2019-10-04 05:15:19	2019-10-03 17:56:47	22.30497	25.62825	5.013362	38.44238	3.598161	3.490994	294.6068	930.0938	614.4769	616.6036	1252.6851	617.1981	24IS
10	10	11	8.0	35.46366	12.45806	30.36570	12.54619	6.338948	2.4670974	242.92564	100.36953	-4.8642478	4.064647	2019-10-05 19:00:00	2019-10-06 03:00:00	2019-10-06 05:13:12	2019-10-05 17:54:19	27.47179	21.80332	5.755258	174.42652	-5.645741	1.117411	240.9499	1549.1065	682.7247	483.1351	5293.5327	2619.1217	24IS
11	11	12	2.5	32.09210	13.01918	33.04650	16.77051	17.504190	2.8917670	82.61625	41.92626	5.5764611	16.592159	2019-10-11 21:30:00	2019-10-12 00:00:00	2019-10-12 05:12:53	2019-10-11 17:48:38	23.95550	10.76127	14.695549	26.90318	-11.248766	9.456449	245.4333	1841.4994	840.7139	593.9665	3140.1573	1619.7243	24IS
12	12	13	0.5	32.30848	22.90317	29.60268	28.98870	9.606418	2.3961741	14.80134	14.49435	-1.2707680	9.521996	2019-10-12 21:00:00	2019-10-12 21:30:00	2019-10-13 05:13:01	2019-10-12 17:47:00	10.55381	0.00000	10.553810	0.00000	NA	NA	301.3149	742.4831	521.8990	521.8990	441.1682	441.1682	24IS
13	13	14	3.0	31.99630	16.70220	32.55915	17.50869	13.754486	4.5048008	97.67744	52.52607	3.4632509	13.311340	2019-10-13 23:30:00	2019-10-14 02:30:00	2019-10-14 05:15:10	2019-10-13 17:46:22	31.09907	17.93545	14.901379	53.80634	-11.804025	9.094839	282.7224	647.0730	375.5769	327.6826	922.5301	482.1588	24IS